{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# d) 重新调整弱学习器数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dpath = './data/'\n",
    "\n",
    "train = pd.read_csv(dpath+'RentListingInquries_FE_train.csv')\n",
    "# train = train.iloc[:100,:]\n",
    "y_train = train['interest_level']\n",
    "X_train = train.drop('interest_level',axis=1)\n",
    "X_train = np.array(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare cross validation \n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "kfold = list(kfold.split(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use cv built in xgboost, cross validate n_estimators quickly\n",
    "def modelfit(alg, X_train, y_train, cv_folds=None, early_stopping_rounds=10):\n",
    "    xgb_param = alg.get_xgb_params()\n",
    "    xgb_param['num_class'] = 3\n",
    "    \n",
    "    #直接调用xgboost，而非sklarn的wrapper类\n",
    "    xgtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "        \n",
    "    cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds,\n",
    "             metrics='mlogloss', early_stopping_rounds=early_stopping_rounds)\n",
    "  \n",
    "    cvresult.to_csv('4_nestimators.csv', index_label = 'n_estimators')\n",
    "    \n",
    "    #最佳参数n_estimators\n",
    "    n_estimators = cvresult.shape[0]\n",
    "    \n",
    "    # 采用交叉验证得到的最佳参数n_estimators，训练模型\n",
    "    alg.set_params(n_estimators = n_estimators)\n",
    "    alg.fit(X_train, y_train, eval_metric='mlogloss')\n",
    "        \n",
    "    #Predict training set:\n",
    "    train_predprob = alg.predict_proba(X_train)\n",
    "    logloss = log_loss(y_train, train_predprob)\n",
    "\n",
    "    #Print model report:\n",
    "    print (\"logloss of train :\" )\n",
    "    print (logloss)\n",
    "    print (\"Best estimators :\")\n",
    "    print (n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of train :\n",
      "0.484035698562\n",
      "Best estimators :\n",
      "181\n"
     ]
    }
   ],
   "source": [
    "xgb4 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=400,  #设置的大一些\n",
    "        max_depth=7, # 根据上一轮结果，得到最优深度是7，weight是7\n",
    "        min_child_weight=7,\n",
    "        reg_alpha=1,\n",
    "        reg_lambda=0.75,# 根据上一轮结果，给出正则参数\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "modelfit(xgb4, X_train, y_train, cv_folds = kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hXrSISN7JWSi4exdwFfAwsAi4290Xmtn1ZnZONNkpwGIzewU4APhKruoZSLL2QOpsE8vW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\nNz1FS1snzW1drG9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LYPuGXV9XecDAoTFyEowYk/OtDYWCiMhgS78yvO7IzNN0tkHz6syh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\n6aVQEBGRXgoFERHppVAQEZFe/z/YSWOCcqMo8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17cee5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('4_nestimators.csv')\n",
    "        \n",
    "# plot\n",
    "test_means = cvresult['test-mlogloss-mean']\n",
    "test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-mlogloss-mean']\n",
    "train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "x_axis = range(0, cvresult.shape[0])\n",
    "        \n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators4_1.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XStTqiVojUWs0WDda44hTsoVPPv+ax7NutMY967KetbvXjXHPuiqrhka54a61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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114867588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('4_nestimators.csv')\n",
    "\n",
    "cvresult = cvresult.iloc[100:]\n",
    "# plot\n",
    "test_means = cvresult['test-mlogloss-mean']\n",
    "test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-mlogloss-mean']\n",
    "train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "x_axis = range(100,cvresult.shape[0]+100)\n",
    "        \n",
    "fig = pyplot.figure(figsize=(10, 10), dpi=100)\n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators_detail_4.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
